Overview

Dataset statistics

Number of variables21
Number of observations921
Missing cells2856
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory144.9 KiB
Average record size in memory161.1 B

Variable types

Categorical11
Unsupported1
Numeric8
Boolean1

Alerts

Heart rate (bpm) has constant value "0" Constant
Start has a high cardinality: 921 distinct values High cardinality
End has a high cardinality: 921 distinct values High cardinality
Sleep Quality has a high cardinality: 65 distinct values High cardinality
Window start has a high cardinality: 840 distinct values High cardinality
Window stop has a high cardinality: 840 distinct values High cardinality
Time in bed (seconds) is highly correlated with Sleep Quality and 4 other fieldsHigh correlation
Time asleep (seconds) is highly correlated with Sleep Quality and 4 other fieldsHigh correlation
Sleep Quality is highly correlated with Regularity and 5 other fieldsHigh correlation
Regularity is highly correlated with Sleep Quality and 5 other fieldsHigh correlation
Steps is highly correlated with NotesHigh correlation
Alarm mode is highly correlated with Time asleep (seconds)High correlation
Air Pressure (Pa) is highly correlated with Sleep Quality and 5 other fieldsHigh correlation
City is highly correlated with Sleep Quality and 2 other fieldsHigh correlation
Time before sleep (seconds) is highly correlated with NotesHigh correlation
Weather temperature (°C) is highly correlated with Air Pressure (Pa) and 1 other fieldsHigh correlation
Weather type is highly correlated with Air Pressure (Pa) and 1 other fieldsHigh correlation
Notes is highly correlated with Sleep Quality and 5 other fieldsHigh correlation
Mood has 921 (100.0%) missing values Missing
Air Pressure (Pa) has 429 (46.6%) missing values Missing
City has 434 (47.1%) missing values Missing
Window start has 80 (8.7%) missing values Missing
Window stop has 80 (8.7%) missing values Missing
Notes has 912 (99.0%) missing values Missing
Start is uniformly distributed Uniform
End is uniformly distributed Uniform
Window start is uniformly distributed Uniform
Window stop is uniformly distributed Uniform
Start has unique values Unique
End has unique values Unique
Mood is an unsupported type, check if it needs cleaning or further analysis Unsupported
Steps has 11 (1.2%) zeros Zeros
Air Pressure (Pa) has 133 (14.4%) zeros Zeros
Snore time has 291 (31.6%) zeros Zeros
Weather temperature (°C) has 489 (53.1%) zeros Zeros

Reproduction

Analysis started2023-03-26 21:57:41.115231
Analysis finished2023-03-26 21:57:56.118303
Duration15 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Start
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct921
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2019-05-12 23:26:13
 
1
2021-03-11 23:48:26
 
1
2021-03-14 00:01:11
 
1
2021-03-14 23:12:39
 
1
2021-03-15 21:57:42
 
1
Other values (916)
916 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters17499
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique921 ?
Unique (%)100.0%

Sample

1st row2019-05-12 23:26:13
2nd row2019-05-13 22:10:31
3rd row2019-05-14 21:43:00
4th row2019-05-15 23:11:51
5th row2019-05-16 23:12:13

Common Values

ValueCountFrequency (%)
2019-05-12 23:26:131
 
0.1%
2021-03-11 23:48:261
 
0.1%
2021-03-14 00:01:111
 
0.1%
2021-03-14 23:12:391
 
0.1%
2021-03-15 21:57:421
 
0.1%
2021-03-16 22:17:361
 
0.1%
2021-03-17 22:51:131
 
0.1%
2021-03-18 22:17:281
 
0.1%
2021-03-19 22:41:391
 
0.1%
2021-03-20 23:44:001
 
0.1%
Other values (911)911
98.9%

Length

2023-03-26T17:57:56.178931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22:58:553
 
0.2%
21:42:383
 
0.2%
2020-07-273
 
0.2%
2020-07-302
 
0.1%
2020-06-062
 
0.1%
22:29:222
 
0.1%
2022-01-172
 
0.1%
22:34:042
 
0.1%
22:13:302
 
0.1%
2019-05-262
 
0.1%
Other values (1746)1819
98.8%

Most occurring characters

ValueCountFrequency (%)
24067
23.2%
03006
17.2%
12090
11.9%
-1842
10.5%
:1842
10.5%
921
 
5.3%
3864
 
4.9%
5654
 
3.7%
4618
 
3.5%
9545
 
3.1%
Other values (3)1050
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12894
73.7%
Dash Punctuation1842
 
10.5%
Other Punctuation1842
 
10.5%
Space Separator921
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
24067
31.5%
03006
23.3%
12090
16.2%
3864
 
6.7%
5654
 
5.1%
4618
 
4.8%
9545
 
4.2%
8370
 
2.9%
6341
 
2.6%
7339
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-1842
100.0%
Other Punctuation
ValueCountFrequency (%)
:1842
100.0%
Space Separator
ValueCountFrequency (%)
921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common17499
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
24067
23.2%
03006
17.2%
12090
11.9%
-1842
10.5%
:1842
10.5%
921
 
5.3%
3864
 
4.9%
5654
 
3.7%
4618
 
3.5%
9545
 
3.1%
Other values (3)1050
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24067
23.2%
03006
17.2%
12090
11.9%
-1842
10.5%
:1842
10.5%
921
 
5.3%
3864
 
4.9%
5654
 
3.7%
4618
 
3.5%
9545
 
3.1%
Other values (3)1050
 
6.0%

End
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct921
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2019-05-13 06:11:03
 
1
2021-03-12 06:28:20
 
1
2021-03-14 07:21:47
 
1
2021-03-15 06:09:45
 
1
2021-03-16 05:31:07
 
1
Other values (916)
916 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters17499
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique921 ?
Unique (%)100.0%

Sample

1st row2019-05-13 06:11:03
2nd row2019-05-14 06:10:42
3rd row2019-05-15 06:10:41
4th row2019-05-16 06:13:59
5th row2019-05-17 06:20:32

Common Values

ValueCountFrequency (%)
2019-05-13 06:11:031
 
0.1%
2021-03-12 06:28:201
 
0.1%
2021-03-14 07:21:471
 
0.1%
2021-03-15 06:09:451
 
0.1%
2021-03-16 05:31:071
 
0.1%
2021-03-17 06:14:211
 
0.1%
2021-03-18 06:14:071
 
0.1%
2021-03-19 05:43:461
 
0.1%
2021-03-20 06:46:081
 
0.1%
2021-03-21 06:59:361
 
0.1%
Other values (911)911
98.9%

Length

2023-03-26T17:57:56.263284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06:10:423
 
0.2%
05:01:303
 
0.2%
05:00:363
 
0.2%
05:00:203
 
0.2%
06:10:413
 
0.2%
06:30:283
 
0.2%
05:11:383
 
0.2%
06:20:363
 
0.2%
06:10:503
 
0.2%
06:00:393
 
0.2%
Other values (1762)1812
98.4%

Most occurring characters

ValueCountFrequency (%)
03977
22.7%
22790
15.9%
11908
10.9%
-1842
10.5%
:1842
10.5%
921
 
5.3%
5860
 
4.9%
3703
 
4.0%
4655
 
3.7%
6645
 
3.7%
Other values (3)1356
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12894
73.7%
Dash Punctuation1842
 
10.5%
Other Punctuation1842
 
10.5%
Space Separator921
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03977
30.8%
22790
21.6%
11908
14.8%
5860
 
6.7%
3703
 
5.5%
4655
 
5.1%
6645
 
5.0%
9524
 
4.1%
7467
 
3.6%
8365
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
-1842
100.0%
Other Punctuation
ValueCountFrequency (%)
:1842
100.0%
Space Separator
ValueCountFrequency (%)
921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common17499
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03977
22.7%
22790
15.9%
11908
10.9%
-1842
10.5%
:1842
10.5%
921
 
5.3%
5860
 
4.9%
3703
 
4.0%
4655
 
3.7%
6645
 
3.7%
Other values (3)1356
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII17499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03977
22.7%
22790
15.9%
11908
10.9%
-1842
10.5%
:1842
10.5%
921
 
5.3%
5860
 
4.9%
3703
 
4.0%
4655
 
3.7%
6645
 
3.7%
Other values (3)1356
 
7.7%

Sleep Quality
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct65
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
75%
 
42
78%
 
39
77%
 
38
100%
 
37
82%
 
35
Other values (60)
730 

Length

Max length4
Median length3
Mean length3.036916395
Min length2

Characters and Unicode

Total characters2797
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.2%

Sample

1st row60%
2nd row73%
3rd row86%
4th row77%
5th row78%

Common Values

ValueCountFrequency (%)
75%42
 
4.6%
78%39
 
4.2%
77%38
 
4.1%
100%37
 
4.0%
82%35
 
3.8%
80%35
 
3.8%
79%35
 
3.8%
74%33
 
3.6%
84%28
 
3.0%
81%28
 
3.0%
Other values (55)571
62.0%

Length

2023-03-26T17:57:56.357457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7542
 
4.6%
7839
 
4.2%
7738
 
4.1%
10037
 
4.0%
8235
 
3.8%
8035
 
3.8%
7935
 
3.8%
7433
 
3.6%
8128
 
3.0%
8428
 
3.0%
Other values (55)571
62.0%

Most occurring characters

ValueCountFrequency (%)
%921
32.9%
7412
14.7%
8356
 
12.7%
9222
 
7.9%
6213
 
7.6%
0153
 
5.5%
5127
 
4.5%
1122
 
4.4%
2100
 
3.6%
497
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1876
67.1%
Other Punctuation921
32.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7412
22.0%
8356
19.0%
9222
11.8%
6213
11.4%
0153
 
8.2%
5127
 
6.8%
1122
 
6.5%
2100
 
5.3%
497
 
5.2%
374
 
3.9%
Other Punctuation
ValueCountFrequency (%)
%921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2797
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
%921
32.9%
7412
14.7%
8356
 
12.7%
9222
 
7.9%
6213
 
7.6%
0153
 
5.5%
5127
 
4.5%
1122
 
4.4%
2100
 
3.6%
497
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
%921
32.9%
7412
14.7%
8356
 
12.7%
9222
 
7.9%
6213
 
7.6%
0153
 
5.5%
5127
 
4.5%
1122
 
4.4%
2100
 
3.6%
497
 
3.5%

Regularity
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
89%
 
62
86%
 
57
85%
 
57
88%
 
54
84%
 
52
Other values (45)
639 

Length

Max length4
Median length3
Mean length2.991313789
Min length2

Characters and Unicode

Total characters2755
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.3%

Sample

1st row0%
2nd row0%
3rd row96%
4th row92%
5th row94%

Common Values

ValueCountFrequency (%)
89%62
 
6.7%
86%57
 
6.2%
85%57
 
6.2%
88%54
 
5.9%
84%52
 
5.6%
90%52
 
5.6%
91%51
 
5.5%
92%46
 
5.0%
93%45
 
4.9%
87%45
 
4.9%
Other values (40)400
43.4%

Length

2023-03-26T17:57:56.459095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8962
 
6.7%
8557
 
6.2%
8657
 
6.2%
8854
 
5.9%
8452
 
5.6%
9052
 
5.6%
9151
 
5.5%
9246
 
5.0%
9345
 
4.9%
8745
 
4.9%
Other values (40)400
43.4%

Most occurring characters

ValueCountFrequency (%)
%921
33.4%
8547
19.9%
9373
13.5%
7202
 
7.3%
6113
 
4.1%
5112
 
4.1%
1102
 
3.7%
3102
 
3.7%
4100
 
3.6%
097
 
3.5%
Other values (2)86
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1833
66.5%
Other Punctuation921
33.4%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8547
29.8%
9373
20.3%
7202
 
11.0%
6113
 
6.2%
5112
 
6.1%
1102
 
5.6%
3102
 
5.6%
4100
 
5.5%
097
 
5.3%
285
 
4.6%
Other Punctuation
ValueCountFrequency (%)
%921
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
%921
33.4%
8547
19.9%
9373
13.5%
7202
 
7.3%
6113
 
4.1%
5112
 
4.1%
1102
 
3.7%
3102
 
3.7%
4100
 
3.6%
097
 
3.5%
Other values (2)86
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
%921
33.4%
8547
19.9%
9373
13.5%
7202
 
7.3%
6113
 
4.1%
5112
 
4.1%
1102
 
3.7%
3102
 
3.7%
4100
 
3.6%
097
 
3.5%
Other values (2)86
 
3.1%

Mood
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing921
Missing (%)100.0%
Memory size7.3 KiB

Heart rate (bpm)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
921 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters921
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0921
100.0%

Length

2023-03-26T17:57:56.554088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-26T17:57:56.637975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0921
100.0%

Most occurring characters

ValueCountFrequency (%)
0921
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number921
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0921
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0921
100.0%

Steps
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct877
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5311.599349
Minimum0
Maximum38165
Zeros11
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:56.734593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile290
Q12299
median4245
Q37452
95-th percentile12659
Maximum38165
Range38165
Interquartile range (IQR)5153

Descriptive statistics

Standard deviation4396.822762
Coefficient of variation (CV)0.8277775626
Kurtosis7.144768711
Mean5311.599349
Median Absolute Deviation (MAD)2344
Skewness1.96942204
Sum4891983
Variance19332050.4
MonotonicityNot monotonic
2023-03-26T17:57:56.877547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
1.2%
97373
 
0.3%
1663
 
0.3%
47182
 
0.2%
29922
 
0.2%
2902
 
0.2%
35262
 
0.2%
40912
 
0.2%
44042
 
0.2%
6032
 
0.2%
Other values (867)890
96.6%
ValueCountFrequency (%)
011
1.2%
61
 
0.1%
401
 
0.1%
511
 
0.1%
631
 
0.1%
811
 
0.1%
871
 
0.1%
881
 
0.1%
931
 
0.1%
941
 
0.1%
ValueCountFrequency (%)
381651
0.1%
315931
0.1%
293591
0.1%
246481
0.1%
242731
0.1%
231741
0.1%
223661
0.1%
221141
0.1%
220301
0.1%
218251
0.1%

Alarm mode
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Normal
841 
No alarm
 
80

Length

Max length8
Median length6
Mean length6.173724213
Min length6

Characters and Unicode

Total characters5686
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal841
91.3%
No alarm80
 
8.7%

Length

2023-03-26T17:57:57.004297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-26T17:57:57.105419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal841
84.0%
no80
 
8.0%
alarm80
 
8.0%

Most occurring characters

ValueCountFrequency (%)
a1001
17.6%
N921
16.2%
o921
16.2%
r921
16.2%
m921
16.2%
l921
16.2%
80
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4685
82.4%
Uppercase Letter921
 
16.2%
Space Separator80
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1001
21.4%
o921
19.7%
r921
19.7%
m921
19.7%
l921
19.7%
Uppercase Letter
ValueCountFrequency (%)
N921
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5606
98.6%
Common80
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1001
17.9%
N921
16.4%
o921
16.4%
r921
16.4%
m921
16.4%
l921
16.4%
Common
ValueCountFrequency (%)
80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1001
17.6%
N921
16.2%
o921
16.2%
r921
16.2%
m921
16.2%
l921
16.2%
80
 
1.4%

Air Pressure (Pa)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct46
Distinct (%)9.3%
Missing429
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean68.6601626
Minimum0
Maximum96.5
Zeros133
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:57.200566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median93.7
Q394.4
95-th percentile95.545
Maximum96.5
Range96.5
Interquartile range (IQR)94.4

Descriptive statistics

Standard deviation41.84536754
Coefficient of variation (CV)0.6094562837
Kurtosis-0.9287289508
Mean68.6601626
Median Absolute Deviation (MAD)0.9
Skewness-1.035595174
Sum33780.8
Variance1751.034784
MonotonicityNot monotonic
2023-03-26T17:57:57.317715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0133
 
14.4%
93.825
 
2.7%
94.224
 
2.6%
93.622
 
2.4%
93.920
 
2.2%
9419
 
2.1%
93.714
 
1.5%
93.113
 
1.4%
94.313
 
1.4%
94.613
 
1.4%
Other values (36)196
21.3%
(Missing)429
46.6%
ValueCountFrequency (%)
0133
14.4%
83.51
 
0.1%
83.81
 
0.1%
91.21
 
0.1%
92.11
 
0.1%
92.22
 
0.2%
92.42
 
0.2%
92.62
 
0.2%
92.74
 
0.4%
92.82
 
0.2%
ValueCountFrequency (%)
96.51
 
0.1%
96.31
 
0.1%
96.21
 
0.1%
96.14
0.4%
961
 
0.1%
95.91
 
0.1%
95.89
1.0%
95.74
0.4%
95.63
 
0.3%
95.58
0.9%

City
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.0%
Missing434
Missing (%)47.1%
Memory size7.3 KiB
Nelson
340 
Central Kootenay
139 
Fruitvale
 
5
North Okanagan
 
2
View Royal
 
1

Length

Max length16
Median length6
Mean length8.926078029
Min length6

Characters and Unicode

Total characters4347
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowNelson
2nd rowNelson
3rd rowNelson
4th rowNelson
5th rowNelson

Common Values

ValueCountFrequency (%)
Nelson340
36.9%
Central Kootenay139
 
15.1%
Fruitvale5
 
0.5%
North Okanagan2
 
0.2%
View Royal1
 
0.1%
(Missing)434
47.1%

Length

2023-03-26T17:57:57.427862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-26T17:57:57.532457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
nelson340
54.1%
central139
22.1%
kootenay139
22.1%
fruitvale5
 
0.8%
north2
 
0.3%
okanagan2
 
0.3%
view1
 
0.2%
royal1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e624
14.4%
n622
14.3%
o621
14.3%
l485
11.2%
N342
7.9%
s340
7.8%
a290
6.7%
t285
6.6%
r146
 
3.4%
142
 
3.3%
Other values (14)450
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3576
82.3%
Uppercase Letter629
 
14.5%
Space Separator142
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e624
17.4%
n622
17.4%
o621
17.4%
l485
13.6%
s340
9.5%
a290
8.1%
t285
8.0%
r146
 
4.1%
y140
 
3.9%
i6
 
0.2%
Other values (6)17
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N342
54.4%
K139
22.1%
C139
22.1%
F5
 
0.8%
O2
 
0.3%
V1
 
0.2%
R1
 
0.2%
Space Separator
ValueCountFrequency (%)
142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4205
96.7%
Common142
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e624
14.8%
n622
14.8%
o621
14.8%
l485
11.5%
N342
8.1%
s340
8.1%
a290
6.9%
t285
6.8%
r146
 
3.5%
y140
 
3.3%
Other values (13)310
7.4%
Common
ValueCountFrequency (%)
142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e624
14.4%
n622
14.3%
o621
14.3%
l485
11.2%
N342
7.9%
s340
7.8%
a290
6.7%
t285
6.6%
r146
 
3.4%
142
 
3.3%
Other values (14)450
10.4%

Movements per hour
Real number (ℝ≥0)

Distinct568
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.2774159
Minimum0
Maximum17926.7
Zeros7
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:57.644571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.1
Q137
median49.6
Q365.2
95-th percentile141.7
Maximum17926.7
Range17926.7
Interquartile range (IQR)28.2

Descriptive statistics

Standard deviation822.7762988
Coefficient of variation (CV)4.721072405
Kurtosis245.0269911
Mean174.2774159
Median Absolute Deviation (MAD)13.9
Skewness13.09909146
Sum160509.5
Variance676960.8378
MonotonicityNot monotonic
2023-03-26T17:57:57.768902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
0.8%
355
 
0.5%
58.35
 
0.5%
34.35
 
0.5%
64.45
 
0.5%
39.75
 
0.5%
45.45
 
0.5%
56.55
 
0.5%
40.35
 
0.5%
45.35
 
0.5%
Other values (558)869
94.4%
ValueCountFrequency (%)
07
0.8%
1.81
 
0.1%
2.51
 
0.1%
2.61
 
0.1%
61
 
0.1%
6.71
 
0.1%
6.81
 
0.1%
7.11
 
0.1%
7.51
 
0.1%
8.11
 
0.1%
ValueCountFrequency (%)
17926.71
0.1%
5811.21
0.1%
5268.21
0.1%
5185.81
0.1%
4390.71
0.1%
4381.31
0.1%
43001
0.1%
3868.61
0.1%
3765.11
0.1%
3730.31
0.1%

Time in bed (seconds)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct918
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27505.49761
Minimum1852.8
Maximum46703.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:57.911362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1852.8
5-th percentile22879.7
Q125500.6
median27422.9
Q329530.1
95-th percentile33310.2
Maximum46703.4
Range44850.6
Interquartile range (IQR)4029.5

Descriptive statistics

Standard deviation3916.696288
Coefficient of variation (CV)0.1423968526
Kurtosis10.43678849
Mean27505.49761
Median Absolute Deviation (MAD)2045.8
Skewness-1.361025274
Sum25332563.3
Variance15340509.81
MonotonicityNot monotonic
2023-03-26T17:57:58.030039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25739.22
 
0.2%
27090.82
 
0.2%
28935.22
 
0.2%
24289.21
 
0.1%
27148.71
 
0.1%
27205.81
 
0.1%
28605.31
 
0.1%
26574.31
 
0.1%
26777.71
 
0.1%
29069.31
 
0.1%
Other values (908)908
98.6%
ValueCountFrequency (%)
1852.81
0.1%
2536.21
0.1%
2855.41
0.1%
28631
0.1%
4664.41
0.1%
5510.81
0.1%
7452.51
0.1%
8050.31
0.1%
17424.71
0.1%
17889.91
0.1%
ValueCountFrequency (%)
46703.41
0.1%
43094.31
0.1%
38815.61
0.1%
38656.71
0.1%
37709.11
0.1%
37708.91
0.1%
37530.31
0.1%
37451.11
0.1%
37220.91
0.1%
36899.61
0.1%

Time asleep (seconds)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct915
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23912.68317
Minimum0
Maximum45769.4
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:58.152238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18314
Q121627.5
median23919
Q326257
95-th percentile29929
Maximum45769.4
Range45769.4
Interquartile range (IQR)4629.5

Descriptive statistics

Standard deviation4066.189356
Coefficient of variation (CV)0.1700432079
Kurtosis6.860867389
Mean23912.68317
Median Absolute Deviation (MAD)2301.9
Skewness-0.9234025786
Sum22023581.2
Variance16533895.88
MonotonicityNot monotonic
2023-03-26T17:57:58.271370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.3%
27996.72
 
0.2%
23785.12
 
0.2%
26476.62
 
0.2%
25573.62
 
0.2%
22993.81
 
0.1%
20901.61
 
0.1%
20095.71
 
0.1%
20437.91
 
0.1%
23306.31
 
0.1%
Other values (905)905
98.3%
ValueCountFrequency (%)
03
0.3%
1815.81
 
0.1%
2798.31
 
0.1%
3193.31
 
0.1%
4571.21
 
0.1%
7303.41
 
0.1%
14174.71
 
0.1%
15217.61
 
0.1%
15287.61
 
0.1%
15558.21
 
0.1%
ValueCountFrequency (%)
45769.41
0.1%
36227.91
0.1%
35180.81
0.1%
34922.21
0.1%
34619.11
0.1%
344551
0.1%
33985.41
0.1%
33427.41
0.1%
33061.11
0.1%
33023.51
0.1%

Time before sleep (seconds)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct841
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1001.556135
Minimum0
Maximum5677.7
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:58.393357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile169.1
Q1449.9
median563.7
Q31287
95-th percentile3495.5
Maximum5677.7
Range5677.7
Interquartile range (IQR)837.1

Descriptive statistics

Standard deviation1046.287777
Coefficient of variation (CV)1.044662142
Kurtosis2.18298678
Mean1001.556135
Median Absolute Deviation (MAD)362.6
Skewness1.727527056
Sum922433.2
Variance1094718.113
MonotonicityNot monotonic
2023-03-26T17:57:58.504535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15010
 
1.1%
172.14
 
0.4%
595.43
 
0.3%
171.33
 
0.3%
03
 
0.3%
173.33
 
0.3%
502.53
 
0.3%
201.12
 
0.2%
632.12
 
0.2%
461.62
 
0.2%
Other values (831)886
96.2%
ValueCountFrequency (%)
03
 
0.3%
37.11
 
0.1%
57.11
 
0.1%
93.31
 
0.1%
136.21
 
0.1%
141.91
 
0.1%
1491
 
0.1%
15010
1.1%
150.11
 
0.1%
152.11
 
0.1%
ValueCountFrequency (%)
5677.71
0.1%
4548.71
0.1%
44841
0.1%
4483.41
0.1%
4405.61
0.1%
4391.71
0.1%
4282.71
0.1%
4250.81
0.1%
4237.41
0.1%
4219.81
0.1%

Window start
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct840
Distinct (%)99.9%
Missing80
Missing (%)8.7%
Memory size7.3 KiB
2021-12-18 06:40:00
 
2
2019-05-13 06:00:00
 
1
2021-03-28 08:00:00
 
1
2021-03-18 06:00:00
 
1
2021-03-19 05:30:00
 
1
Other values (835)
835 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters15979
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)99.8%

Sample

1st row2019-05-13 06:00:00
2nd row2019-05-14 05:50:00
3rd row2019-05-15 05:50:00
4th row2019-05-16 05:50:00
5th row2019-05-17 05:50:00

Common Values

ValueCountFrequency (%)
2021-12-18 06:40:002
 
0.2%
2019-05-13 06:00:001
 
0.1%
2021-03-28 08:00:001
 
0.1%
2021-03-18 06:00:001
 
0.1%
2021-03-19 05:30:001
 
0.1%
2021-03-20 06:30:001
 
0.1%
2021-03-21 07:00:001
 
0.1%
2021-03-22 05:00:001
 
0.1%
2021-03-23 05:00:001
 
0.1%
2021-03-24 05:00:001
 
0.1%
Other values (830)830
90.1%
(Missing)80
 
8.7%

Length

2023-03-26T17:57:58.614855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:00:00205
 
12.2%
06:00:00103
 
6.1%
07:00:0095
 
5.6%
06:30:0081
 
4.8%
05:30:0078
 
4.6%
05:50:0059
 
3.5%
04:20:0029
 
1.7%
05:45:0028
 
1.7%
04:45:0024
 
1.4%
07:30:0023
 
1.4%
Other values (865)957
56.9%

Most occurring characters

ValueCountFrequency (%)
05863
36.7%
22105
 
13.2%
-1682
 
10.5%
:1682
 
10.5%
11222
 
7.6%
841
 
5.3%
5734
 
4.6%
3402
 
2.5%
6355
 
2.2%
9320
 
2.0%
Other values (3)773
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11774
73.7%
Dash Punctuation1682
 
10.5%
Other Punctuation1682
 
10.5%
Space Separator841
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05863
49.8%
22105
 
17.9%
11222
 
10.4%
5734
 
6.2%
3402
 
3.4%
6355
 
3.0%
9320
 
2.7%
4309
 
2.6%
7288
 
2.4%
8176
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
-1682
100.0%
Other Punctuation
ValueCountFrequency (%)
:1682
100.0%
Space Separator
ValueCountFrequency (%)
841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15979
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05863
36.7%
22105
 
13.2%
-1682
 
10.5%
:1682
 
10.5%
11222
 
7.6%
841
 
5.3%
5734
 
4.6%
3402
 
2.5%
6355
 
2.2%
9320
 
2.0%
Other values (3)773
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05863
36.7%
22105
 
13.2%
-1682
 
10.5%
:1682
 
10.5%
11222
 
7.6%
841
 
5.3%
5734
 
4.6%
3402
 
2.5%
6355
 
2.2%
9320
 
2.0%
Other values (3)773
 
4.8%

Window stop
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct840
Distinct (%)99.9%
Missing80
Missing (%)8.7%
Memory size7.3 KiB
2021-12-18 06:40:00
 
2
2019-05-13 06:00:00
 
1
2021-03-28 08:00:00
 
1
2021-03-18 06:00:00
 
1
2021-03-19 05:30:00
 
1
Other values (835)
835 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters15979
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)99.8%

Sample

1st row2019-05-13 06:00:00
2nd row2019-05-14 05:50:00
3rd row2019-05-15 05:50:00
4th row2019-05-16 05:50:00
5th row2019-05-17 05:50:00

Common Values

ValueCountFrequency (%)
2021-12-18 06:40:002
 
0.2%
2019-05-13 06:00:001
 
0.1%
2021-03-28 08:00:001
 
0.1%
2021-03-18 06:00:001
 
0.1%
2021-03-19 05:30:001
 
0.1%
2021-03-20 06:30:001
 
0.1%
2021-03-21 07:00:001
 
0.1%
2021-03-22 05:00:001
 
0.1%
2021-03-23 05:00:001
 
0.1%
2021-03-24 05:00:001
 
0.1%
Other values (830)830
90.1%
(Missing)80
 
8.7%

Length

2023-03-26T17:57:58.718086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:00:00204
 
12.1%
06:00:00104
 
6.2%
07:00:0095
 
5.6%
06:30:0080
 
4.8%
05:30:0078
 
4.6%
05:50:0060
 
3.6%
04:20:0029
 
1.7%
05:45:0028
 
1.7%
07:30:0024
 
1.4%
04:45:0024
 
1.4%
Other values (864)956
56.8%

Most occurring characters

ValueCountFrequency (%)
05863
36.7%
22104
 
13.2%
-1682
 
10.5%
:1682
 
10.5%
11222
 
7.6%
841
 
5.3%
5734
 
4.6%
3402
 
2.5%
6355
 
2.2%
9320
 
2.0%
Other values (3)774
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11774
73.7%
Dash Punctuation1682
 
10.5%
Other Punctuation1682
 
10.5%
Space Separator841
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05863
49.8%
22104
 
17.9%
11222
 
10.4%
5734
 
6.2%
3402
 
3.4%
6355
 
3.0%
9320
 
2.7%
4309
 
2.6%
7289
 
2.5%
8176
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
-1682
100.0%
Other Punctuation
ValueCountFrequency (%)
:1682
100.0%
Space Separator
ValueCountFrequency (%)
841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15979
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05863
36.7%
22104
 
13.2%
-1682
 
10.5%
:1682
 
10.5%
11222
 
7.6%
841
 
5.3%
5734
 
4.6%
3402
 
2.5%
6355
 
2.2%
9320
 
2.0%
Other values (3)774
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05863
36.7%
22104
 
13.2%
-1682
 
10.5%
:1682
 
10.5%
11222
 
7.6%
841
 
5.3%
5734
 
4.6%
3402
 
2.5%
6355
 
2.2%
9320
 
2.0%
Other values (3)774
 
4.8%

Did snore
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
True
913 
False
 
8
ValueCountFrequency (%)
True913
99.1%
False8
 
0.9%
2023-03-26T17:57:58.821159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Snore time
Real number (ℝ≥0)

ZEROS

Distinct422
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.9442997
Minimum0
Maximum4477.3
Zeros291
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-03-26T17:57:58.923519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median148
Q3440
95-th percentile1258.8
Maximum4477.3
Range4477.3
Interquartile range (IQR)440

Descriptive statistics

Standard deviation455.4728476
Coefficient of variation (CV)1.455443822
Kurtosis13.16755839
Mean312.9442997
Median Absolute Deviation (MAD)148
Skewness2.896226495
Sum288221.7
Variance207455.5149
MonotonicityNot monotonic
2023-03-26T17:57:59.044697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0291
31.6%
12020
 
2.2%
24018
 
2.0%
48015
 
1.6%
18015
 
1.6%
42014
 
1.5%
6013
 
1.4%
36012
 
1.3%
6609
 
1.0%
3009
 
1.0%
Other values (412)505
54.8%
ValueCountFrequency (%)
0291
31.6%
6013
 
1.4%
612
 
0.2%
61.31
 
0.1%
61.62
 
0.2%
61.93
 
0.3%
622
 
0.2%
62.21
 
0.1%
631
 
0.1%
641
 
0.1%
ValueCountFrequency (%)
4477.31
0.1%
3322.81
0.1%
25541
0.1%
2539.91
0.1%
2453.31
0.1%
2150.41
0.1%
21401
0.1%
2134.61
0.1%
21171
0.1%
21041
0.1%

Weather temperature (°C)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct233
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.142562432
Minimum-19
Maximum24.5
Zeros489
Zeros (%)53.1%
Negative138
Negative (%)15.0%
Memory size7.3 KiB
2023-03-26T17:57:59.168257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile-5.3
Q10
median0
Q33.1
95-th percentile16.1
Maximum24.5
Range43.5
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation6.324558032
Coefficient of variation (CV)2.95186639
Kurtosis1.845812131
Mean2.142562432
Median Absolute Deviation (MAD)0
Skewness0.9778081561
Sum1973.3
Variance40.0000343
MonotonicityNot monotonic
2023-03-26T17:57:59.450145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0489
53.1%
-2.86
 
0.7%
35
 
0.5%
-3.95
 
0.5%
-5.25
 
0.5%
45
 
0.5%
0.55
 
0.5%
13.35
 
0.5%
55
 
0.5%
17.25
 
0.5%
Other values (223)386
41.9%
ValueCountFrequency (%)
-191
0.1%
-17.51
0.1%
-16.72
0.2%
-15.21
0.1%
-14.81
0.1%
-14.21
0.1%
-13.31
0.1%
-12.72
0.2%
-12.62
0.2%
-12.51
0.1%
ValueCountFrequency (%)
24.51
0.1%
23.91
0.1%
23.81
0.1%
22.81
0.1%
22.61
0.1%
22.51
0.1%
21.71
0.1%
21.32
0.2%
211
0.1%
20.72
0.2%

Weather type
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
No weather
488 
Sunny
121 
Cloudy
75 
Partly cloudy
71 
Fog
59 
Other values (4)
107 

Length

Max length13
Median length10
Mean length8.142236699
Min length3

Characters and Unicode

Total characters7499
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo weather
2nd rowNo weather
3rd rowNo weather
4th rowNo weather
5th rowNo weather

Common Values

ValueCountFrequency (%)
No weather488
53.0%
Sunny121
 
13.1%
Cloudy75
 
8.1%
Partly cloudy71
 
7.7%
Fog59
 
6.4%
Rain37
 
4.0%
Fair35
 
3.8%
Snow31
 
3.4%
Rainy showers4
 
0.4%

Length

2023-03-26T17:57:59.556793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-26T17:57:59.669576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no488
32.9%
weather488
32.9%
cloudy146
 
9.8%
sunny121
 
8.2%
partly71
 
4.8%
fog59
 
4.0%
rain37
 
2.5%
fair35
 
2.4%
snow31
 
2.1%
rainy4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e980
13.1%
o728
9.7%
a635
 
8.5%
r598
 
8.0%
563
 
7.5%
t559
 
7.5%
w523
 
7.0%
h492
 
6.6%
N488
 
6.5%
y342
 
4.6%
Other values (13)1591
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6015
80.2%
Uppercase Letter921
 
12.3%
Space Separator563
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e980
16.3%
o728
12.1%
a635
10.6%
r598
9.9%
t559
9.3%
w523
8.7%
h492
8.2%
y342
 
5.7%
n314
 
5.2%
u267
 
4.4%
Other values (6)577
9.6%
Uppercase Letter
ValueCountFrequency (%)
N488
53.0%
S152
 
16.5%
F94
 
10.2%
C75
 
8.1%
P71
 
7.7%
R41
 
4.5%
Space Separator
ValueCountFrequency (%)
563
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6936
92.5%
Common563
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e980
14.1%
o728
10.5%
a635
9.2%
r598
8.6%
t559
8.1%
w523
7.5%
h492
 
7.1%
N488
 
7.0%
y342
 
4.9%
n314
 
4.5%
Other values (12)1277
18.4%
Common
ValueCountFrequency (%)
563
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e980
13.1%
o728
9.7%
a635
 
8.5%
r598
 
8.0%
563
 
7.5%
t559
 
7.5%
w523
 
7.0%
h492
 
6.6%
N488
 
6.5%
y342
 
4.6%
Other values (13)1591
21.2%

Notes
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)88.9%
Missing912
Missing (%)99.0%
Memory size7.3 KiB
Coffee:Tea:Worked out
Coffee
Ate late:Coffee:Tea:Worked out
Alcohol:Coffee:Tea:Worked out
Alcohol:Ate late:Coffee
Other values (3)

Length

Max length30
Median length21
Mean length18.66666667
Min length6

Characters and Unicode

Total characters168
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)77.8%

Sample

1st rowCoffee
2nd rowAte late:Coffee:Tea:Worked out
3rd rowCoffee:Tea:Worked out
4th rowCoffee:Tea:Worked out
5th rowAlcohol:Coffee:Tea:Worked out

Common Values

ValueCountFrequency (%)
Coffee:Tea:Worked out2
 
0.2%
Coffee1
 
0.1%
Ate late:Coffee:Tea:Worked out1
 
0.1%
Alcohol:Coffee:Tea:Worked out1
 
0.1%
Alcohol:Ate late:Coffee1
 
0.1%
Alcohol:Coffee1
 
0.1%
Ate late:Coffee1
 
0.1%
Kathryn’s1
 
0.1%
(Missing)912
99.0%

Length

2023-03-26T17:57:59.795279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-26T17:57:59.933571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
out4
25.0%
coffee:tea:worked2
12.5%
ate2
12.5%
late:coffee2
12.5%
coffee1
 
6.2%
late:coffee:tea:worked1
 
6.2%
alcohol:coffee:tea:worked1
 
6.2%
alcohol:ate1
 
6.2%
alcohol:coffee1
 
6.2%
kathryn’s1
 
6.2%

Most occurring characters

ValueCountFrequency (%)
e30
17.9%
o22
13.1%
f16
9.5%
:14
 
8.3%
t11
 
6.5%
l9
 
5.4%
C8
 
4.8%
a8
 
4.8%
7
 
4.2%
A6
 
3.6%
Other values (13)37
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123
73.2%
Uppercase Letter23
 
13.7%
Other Punctuation14
 
8.3%
Space Separator7
 
4.2%
Final Punctuation1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e30
24.4%
o22
17.9%
f16
13.0%
t11
 
8.9%
l9
 
7.3%
a8
 
6.5%
r5
 
4.1%
k4
 
3.3%
d4
 
3.3%
u4
 
3.3%
Other values (5)10
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
C8
34.8%
A6
26.1%
W4
17.4%
T4
17.4%
K1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
:14
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin146
86.9%
Common22
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e30
20.5%
o22
15.1%
f16
11.0%
t11
 
7.5%
l9
 
6.2%
C8
 
5.5%
a8
 
5.5%
A6
 
4.1%
r5
 
3.4%
k4
 
2.7%
Other values (10)27
18.5%
Common
ValueCountFrequency (%)
:14
63.6%
7
31.8%
1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII167
99.4%
Punctuation1
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e30
18.0%
o22
13.2%
f16
9.6%
:14
 
8.4%
t11
 
6.6%
l9
 
5.4%
C8
 
4.8%
a8
 
4.8%
7
 
4.2%
A6
 
3.6%
Other values (12)36
21.6%
Punctuation
ValueCountFrequency (%)
1
100.0%

Interactions

2023-03-26T17:57:54.180510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:47.659981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.781181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.583654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.512967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.535386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.413830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.316245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.297115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:47.856339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.893221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.722206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.664317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.660705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.538017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.427920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.393671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:47.997421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.988752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.823753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.784353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.771272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.644101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.523787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.509519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.139507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.091451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.950986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.005570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.887252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.779518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.642465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.618817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.271148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.181313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.062364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.109832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.992859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.884712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.762962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.733185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.404789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.274924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.173728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.215008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.096153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.996096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.868820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.851821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.532612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.376472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.288966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.323399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.204004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.105013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.980223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.957936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:48.660096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:49.480890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:50.401023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:51.428607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:52.306317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:53.207984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-26T17:57:54.076802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-26T17:58:00.072385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-03-26T17:58:00.251685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-03-26T17:58:00.430235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-03-26T17:58:00.616193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-03-26T17:57:55.270935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-26T17:57:55.636446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-26T17:57:55.865177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-03-26T17:57:55.985493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

StartEndSleep QualityRegularityMoodHeart rate (bpm)StepsAlarm modeAir Pressure (Pa)CityMovements per hourTime in bed (seconds)Time asleep (seconds)Time before sleep (seconds)Window startWindow stopDid snoreSnore timeWeather temperature (°C)Weather typeNotes
02019-05-12 23:26:132019-05-13 06:11:0360%0%NaN08350NormalNaNNaN35.024289.222993.8161.92019-05-13 06:00:002019-05-13 06:00:00True92.00.0No weatherNaN
12019-05-13 22:10:312019-05-14 06:10:4273%0%NaN04746NormalNaNNaN78.628810.225160.9192.12019-05-14 05:50:002019-05-14 05:50:00True0.00.0No weatherNaN
22019-05-14 21:43:002019-05-15 06:10:4186%96%NaN04007NormalNaNNaN60.530461.528430.8203.12019-05-15 05:50:002019-05-15 05:50:00True74.00.0No weatherNaN
32019-05-15 23:11:512019-05-16 06:13:5977%92%NaN06578NormalNaNNaN45.225327.623132.5168.92019-05-16 05:50:002019-05-16 05:50:00True0.00.0No weatherNaN
42019-05-16 23:12:132019-05-17 06:20:3278%94%NaN04913NormalNaNNaN44.625698.422614.6171.32019-05-17 05:50:002019-05-17 05:50:00True188.00.0No weatherNaN
52019-05-19 01:25:122019-05-19 08:43:1172%80%NaN04020No alarmNaNNaN58.026278.220759.8175.2NaNNaNTrue0.00.0No weatherNaN
62019-05-20 22:41:132019-05-21 06:22:5873%58%NaN05133NormalNaNNaN64.027705.224565.2184.72019-05-21 05:50:002019-05-21 05:50:00True0.00.0No weatherNaN
72019-05-21 22:39:272019-05-22 06:00:5578%77%NaN04927NormalNaNNaN51.026488.822780.4176.62019-05-22 05:50:002019-05-22 05:50:00True279.00.0No weatherNaN
82019-05-22 22:36:592019-05-23 06:03:5984%98%NaN06637NormalNaNNaN43.526819.925925.9178.82019-05-23 05:50:002019-05-23 05:50:00True0.00.0No weatherNaN
92019-05-23 23:15:232019-05-24 06:33:4788%95%NaN04298NormalNaNNaN31.626304.223761.4175.42019-05-24 06:35:002019-05-24 06:35:00True0.00.0No weatherNaN

Last rows

StartEndSleep QualityRegularityMoodHeart rate (bpm)StepsAlarm modeAir Pressure (Pa)CityMovements per hourTime in bed (seconds)Time asleep (seconds)Time before sleep (seconds)Window startWindow stopDid snoreSnore timeWeather temperature (°C)Weather typeNotes
9112022-03-17 21:39:472022-03-18 04:40:5774%95%NaN02045Normal94.7Central Kootenay39.725269.421815.9505.42022-03-18 04:20:002022-03-18 04:20:00True537.43.6RainNaN
9122022-03-19 23:41:342022-03-20 07:03:1983%80%NaN01985Normal94.1Central Kootenay34.626505.323413.0530.12022-03-20 07:00:002022-03-20 07:00:00True480.01.1FogNaN
9132022-03-20 22:01:312022-03-21 04:21:0174%63%NaN0741Normal95.1Central Kootenay31.622770.520189.9455.42022-03-21 04:20:002022-03-21 04:20:00True300.01.3CloudyNaN
9142022-03-21 21:23:092022-03-22 04:21:5282%79%NaN04675Normal95.6Central Kootenay30.925122.621689.2502.52022-03-22 04:20:002022-03-22 04:20:00True417.03.0FogNaN
9152022-03-23 22:30:252022-03-24 06:03:0772%86%NaN02521Normal94.5Central Kootenay45.127162.025079.6543.22022-03-24 04:45:002022-03-24 04:45:00True1080.04.6FogNaN
9162022-03-24 21:33:462022-03-25 04:21:4071%77%NaN03903Normal95.2Central Kootenay39.724474.120803.0489.52022-03-25 04:20:002022-03-25 04:20:00True695.74.0CloudyNaN
9172022-03-25 16:48:052022-03-25 17:30:229%14%NaN0495Normal83.8North Okanagan0.02536.20.00.02022-03-25 17:30:002022-03-25 17:30:00True0.05.0CloudyNaN
9182022-03-26 21:14:232022-03-27 06:11:0149%-1%NaN013388Normal83.5North Okanagan82.732198.124577.93649.12022-03-27 06:00:002022-03-27 06:00:00True506.4-1.1CloudyNaN
9192022-03-28 22:53:232022-03-29 04:50:3677%22%NaN0456Normal93.9Central Kootenay17.321433.615860.8428.72022-03-29 04:20:002022-03-29 04:20:00True60.06.1SunnyNaN
9202022-03-29 22:44:092022-03-30 05:11:1568%85%NaN05156Normal94.4Central Kootenay38.223225.918813.0464.52022-03-30 05:00:002022-03-30 05:00:00True720.010.5CloudyNaN